Machine learning (ML)-based lithography optimizations

Seongbo Shim, Suhyeong Choi, Youngsoo Shin
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引用次数: 8

Abstract

Recent lithography optimizations demand higher accuracy and cause longer runtime. Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for example, take a few days due to lengthy lithography simulations and high pattern density. Etch proximity correction (EPC) is another example of intensive optimization due to a complex physical model of etching process. Machine learning has recently been applied to these lithography optimizations with some success. In this paper, we introduce basic algorithms of machine learning technique, e.g. support vector machine (SVM) and neural networks, and how they are applied to lithography optimization problems. Discussion on learning parameters, preparation of compact learning data set, technique to avoid over-fitting are also provided.
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基于机器学习(ML)的光刻优化
最近的光刻优化要求更高的精度,并导致更长的运行时间。例如,光学接近校正(OPC)和亚分辨率辅助特征(SRAF)插入需要几天时间,因为光刻模拟时间长,图案密度高。由于蚀刻过程的物理模型复杂,蚀刻邻近校正(EPC)是强化优化的另一个例子。机器学习最近被应用于这些光刻优化,并取得了一些成功。本文介绍了机器学习技术的基本算法,如支持向量机(SVM)和神经网络,以及它们如何应用于光刻优化问题。讨论了学习参数、紧凑学习数据集的制备以及避免过拟合的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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